Combining Local and Global Features for Offline Handwriting Recognition

Rhandley D. Cajote, Rowena Cristina L. Guevara

Abstract

The result of investigating the performance of handwriting recognition systems using local features, global features and a combination of local and global features is presented. The global features are derived from the shape of the word contour and the local features are derived from the geometric characteristics of the word segments. The global features utilize the centroidal distance of the word contour also known as the polar-radii graph or PRG. The system was trained and tested using the demo version of the publicly available IAM database. Using the local features alone and an HMM recognizer a recognition rate of 58% was achieved using a 20-word vocabulary. Using the PRG of the word contour as a global feature and an MLP classifier a recognition rate of 78% is achieved. The PRG is then combined with the local features using a combined probabilistic framework and the hybrid handwritten word recognition system achieved a recognition rate of 72%.